Book Image

Python Object-Oriented Programming - Fourth Edition

By : Steven F. Lott, Dusty Phillips
2 (2)
Book Image

Python Object-Oriented Programming - Fourth Edition

2 (2)
By: Steven F. Lott, Dusty Phillips

Overview of this book

Object-oriented programming (OOP) is a popular design paradigm in which data and behaviors are encapsulated in such a way that they can be manipulated together. Python Object-Oriented Programming, Fourth Edition dives deep into the various aspects of OOP, Python as an OOP language, common and advanced design patterns, and hands-on data manipulation and testing of more complex OOP systems. These concepts are consolidated by open-ended exercises, as well as a real-world case study at the end of every chapter, newly written for this edition. All example code is now compatible with Python 3.9+ syntax and has been updated with type hints for ease of learning. Steven and Dusty provide a comprehensive, illustrative tour of important OOP concepts, such as inheritance, composition, and polymorphism, and explain how they work together with Python’s classes and data structures to facilitate good design. In addition, the book also features an in-depth look at Python’s exception handling and how functional programming intersects with OOP. Two very powerful automated testing systems, unittest and pytest, are introduced. The final chapter provides a detailed discussion of Python's concurrent programming ecosystem. By the end of the book, you will have a thorough understanding of how to think about and apply object-oriented principles using Python syntax and be able to confidently create robust and reliable programs.
Table of Contents (17 chapters)
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Case study

We'll review a piece of the case study we set aside in Chapter 3, When Objects Are Alike. We talked about the various ways to compute distances, but left part of the design to be filled in later. Now that we've seen some of the basic design patterns, we can apply some of them to our evolving case study.

Specifically, we need to put the various kinds of distance computations into the Hyperparameter class definition. In Chapter 3, we introduced the idea that the distance computation is not a single definition. There are over 50 commonly used distance computation alternatives, some simple, some rather complex. In Chapter 3, we showed a few common ones, including Euclidean distance, Manhattan distance, Chebyshev distance, and even a complex-looking Sorensen distance. Each weights the "nearness" of the neighbors slightly differently.

This leads us to look at the Hyperparameter class as containing three important components: